Profiling and Supporting Adaptive Micro Learning on Open Education Resources

Sun, G; Cui, T; Beydoun, G; Shen, J; Chen, S

Profiling and Supporting Adaptive Micro Learning on Open Education Resources

Sun, G Cui, T Beydoun, G

Shen, J

Chen, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2016 International Conference on Advanced Cloud and Big Data, CBD 2016, 2017, pp. 158 - 163
Issue Date:: 2017-01-11

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Field	Value	Language
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Cui, T	en_US
dc.contributor.author	Beydoun, G https://orcid.org/0000-0001-8087-5445	en_US
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140	en_US
dc.contributor.author	Chen, S	en_US
dc.date.issued	2017-01-11	en_US
dc.identifier.citation	Proceedings - 2016 International Conference on Advanced Cloud and Big Data, CBD 2016, 2017, pp. 158 - 163	en_US
dc.identifier.isbn	9781509036776	en_US
dc.identifier.uri	http://hdl.handle.net/10453/92749
dc.description.abstract	© 2016 IEEE. It is found that learners prefer to use micro learning mode to conduct learning activities through open educational resources (OERs). However, adaptive micro learning is scarcely supported by current OER platforms. In this paper we focus on profiling an effective micro learning process which is central to establish the raw materials and set up rules for the final adaptive process. This work consists of two parts. First, we conducted an educational data mining and learning analysis study to discover the patterns and rules in micro learning through OER. Then based on its findings, we profiled features of both learners and OERs to reveal the full learning story in order to support the decision making process. Incorporating educational data mining and learning analysis, an cloud-based architecture for Micro Learning as a Service (MLaaS) was designed to integrate all necessary procedures together as a complete service for delivering micro OERs. The MLaaS also provides a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, was also introduced.	en_US
dc.relation.ispartof	Proceedings - 2016 International Conference on Advanced Cloud and Big Data, CBD 2016	en_US
dc.relation.isbasedon	10.1109/CBD.2016.037	en_US
dc.title	Profiling and Supporting Adaptive Micro Learning on Open Education Resources	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. It is found that learners prefer to use micro learning mode to conduct learning activities through open educational resources (OERs). However, adaptive micro learning is scarcely supported by current OER platforms. In this paper we focus on profiling an effective micro learning process which is central to establish the raw materials and set up rules for the final adaptive process. This work consists of two parts. First, we conducted an educational data mining and learning analysis study to discover the patterns and rules in micro learning through OER. Then based on its findings, we profiled features of both learners and OERs to reveal the full learning story in order to support the decision making process. Incorporating educational data mining and learning analysis, an cloud-based architecture for Micro Learning as a Service (MLaaS) was designed to integrate all necessary procedures together as a complete service for delivering micro OERs. The MLaaS also provides a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, was also introduced.

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http://hdl.handle.net/10453/92749